File size: 4,368 Bytes
a92b335 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | from collections.abc import Callable
import torch
import torch.nn as nn
from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
from transformers.models.mistral.modeling_mistral import (
MistralAttention,
MistralDecoderLayer,
MistralForCausalLM,
MistralMLP,
MistralModel,
MistralRMSNorm,
apply_rotary_pos_emb,
eager_attention_forward,
)
try:
from .configuration_q import QConfig
except ImportError: # запуск сгенерированного файла прямо из каталога ноутбука
from configuration_q import QConfig
class QScalarGate(nn.Module):
def __init__(self, hidden_size, multiplier=2.0):
super().__init__()
self.projection = nn.Linear(hidden_size, 1, bias=False)
self.multiplier = multiplier
def forward(self, branch, residual_input):
return branch * self.multiplier * torch.sigmoid(self.projection(residual_input))
class QMLP(MistralMLP):
def __init__(self, config):
super().__init__(config)
self.output_gate = (
QScalarGate(config.hidden_size, config.gate_multiplier)
if config.mlp_scalar_gate else None
)
def forward(self, hidden_states):
output = super().forward(hidden_states)
if self.output_gate is not None:
output = self.output_gate(output, hidden_states)
return output
class QAttention(MistralAttention):
def __init__(self, config, layer_idx):
super().__init__(config, layer_idx)
self.use_rope = layer_idx not in config.nope_layers
self.q_norm = MistralRMSNorm(self.head_dim, eps=config.rms_norm_eps) if config.qk_norm else None
self.k_norm = MistralRMSNorm(self.head_dim, eps=config.rms_norm_eps) if config.qk_norm else None
self.output_gate = (
QScalarGate(config.hidden_size, config.gate_multiplier)
if config.attention_scalar_gate else None
)
def forward(self, hidden_states, position_embeddings, attention_mask, past_key_values=None, **kwargs):
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, self.head_dim)
query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
if self.q_norm is not None:
query_states = self.q_norm(query_states)
key_states = self.k_norm(key_states)
if self.use_rope:
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_values is not None:
key_states, value_states = past_key_values.update(
key_states, value_states, self.layer_idx
)
attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
self.config._attn_implementation, eager_attention_forward
)
attn_output, attn_weights = attention_interface(
self, query_states, key_states, value_states, attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
sliding_window=getattr(self.config, "sliding_window", None),
**kwargs,
)
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
attn_output = self.o_proj(attn_output)
if self.output_gate is not None:
attn_output = self.output_gate(attn_output, hidden_states)
return attn_output, attn_weights
class QDecoderLayer(MistralDecoderLayer):
def __init__(self, config, layer_idx):
super().__init__(config, layer_idx)
self.self_attn = QAttention(config, layer_idx)
self.mlp = QMLP(config)
class QModel(MistralModel):
config_class = QConfig
def __init__(self, config):
super().__init__(config)
self.layers = nn.ModuleList(
[QDecoderLayer(config, i) for i in range(config.num_hidden_layers)]
)
self.post_init()
class QForCausalLM(MistralForCausalLM):
config_class = QConfig
def __init__(self, config):
super().__init__(config)
self.model = QModel(config)
self.post_init()
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